Comparison and rapid prediction of lignocellulose and organic elements of a wide variety of rice straw based on near infrared spectroscopy
Keywords:
rice straw, near infrared reflectance spectroscopy models, rapid prediction, competitive adaptive reweighted sampling, partial least-squares, lignocelluloseAbstract
Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy. Rapid prediction of the lignocellulose (cellulose, hemicellulose, and lignin) and organic elements (carbon, hydrogen, nitrogen, and sulfur) of rice straw would help to decipher its growth mechanisms and thereby improve its sustainable usages. In this study, 364 rice straw samples featuring different rice subspecies (japonica and indica), growing seasons (early-, middle-, and late-season), and growing environments (irrigated and rainfed) were collected, the differences among which were examined by multivariate analysis of variance. Statistic results showed that the cellulose content exhibited significant differences among different growing seasons at a significant level (p < 0.01), and the contents of cellulose and nitrogen had significant differences between different growing environments (p < 0.01). Near infrared reflectance spectroscopy (NIRS) models for predicting the lignocellulosic and organic elements were developed based on two algorithms including partial least squares (PLS) and competitive adaptive reweighted sampling-partial least squares (CARS-PLS). Modeling results showed that most CARS-PLS models are of higher accuracy than the PLS models, possibly because the CARS-PLS models selected optimal combinations of wavenumbers, which might have enhanced the signal of chemical bonds and thereby improved the predictive efficiency. As a major contributor to the applications of rice straw, the nitrogen content was predicted precisely by the CARS-PLS model. Generally, the CARS-PLS models efficiently quantified the lignocellulose and organic elements of a wide variety of rice straw. The acceptable accuracy of the models allowed their practical applications. Keywords: rice straw, near infrared reflectance spectroscopy models, rapid prediction, competitive adaptive reweighted sampling, partial least-squares, lignocellulose DOI: 10.25165/j.ijabe.20191202.4374 Citation: Diallo A A, Yang Z L, Shen G H, Ge J Y, Li Z C, Han L J. Comparison and rapid prediction of lignocellulose and organic elements of a wide variety of rice straw based on near infrared spectroscopy. Int J Agric & Biol Eng, 2019; 12(2): 166–172.References
[1] Abraham A, Mathew A K, Sindhu R, Pandey A, Binod P. Potential of rice straw for bio-refining: An overview. Bioresour Technol., 2016; 215: 29–36. doi: org/10.1016/j.biortech.2016.04.011
[2] Jin S Y, Chen H Z. Near-infrared analysis of the chemical composition of rice straw. Ind Crop Prod., 2007; 26(2): 207–211. doi: 10.1016/ j.indcrop.2007.03.004
[3] Huang C, Han L, Yang Z, Liu X. Ultimate analysis and heating value prediction of straw by near infrared spectroscopy. Waste Manage., 2009; 29(6): 1793–1797. doi: 10.1016/j.wasman.2008.11.027
[4] Huang C, Han L J, Liu X, Ma L. The rapid estimation of cellulose, hemicellulose, and lignin contents in rice straw by near infrared spectroscopy. Energ Source Part A., 2010; 33(2): 114–120. doi: org /10.1080/15567030902937127
[5] Hattori T, Murakami S, Mukai M, Yamada T, Hirochika H, Ike M, et al. Rapid analysis of transgenic rice straw using near-infrared spectroscopy. Plant Biotechnol-Nar., 2012; 29: 359–366. doi: org/10.5511/plantbiotech- nology.12.0501a
[6] Sluiter J B, Ruiz R O, Scarlata C J, Sluiter A D, Templeton D W. Compositional analysis of lignocellulosic feedstocks. 1. Review and Description of Methods. J Agric Food Chem., 2010; 58: 9043–9053. doi: 10.1021/jf1008023
[7] Roggo Y, Chalus P, Maurer L, Lema-Martinez C, Edmond A, Jent N. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J Pharmaceut Biomed., 2007; 44: 683–700. doi: org/10. 1016/j.jpba.2007.03.023
[8] Huang C J, Han L J, Yang Z L, Liu X. Prediction of heating value of straw by proximate data, and near infrared spectroscopy. Energ Convers Manage., 2008; 49(12): 3433–3438. doi: 10.1016/j.enconman.2008. 08.020
[9] Belal E B. Bioethanol production from rice straw residues. Braz J Microbiol., 2013; 44: 225–234. doi: 10.1590/S1517-83822013000100033
[10] Liao C P, Wu C Z, Yany Y J, Huang H T. Chemical elemental characteristics of biomass fuels in China. Biomass Bioenerg., 2004; 27(2): 119–130. doi: 10.1016/j.biombioe.2004.01.002
[11] Banta S, Mendoza C. Organic Matter and Rice. Manila, Philippines: International Rice Research Institute, 1984.
[12] Garivait S, Chaiyo U, Patumsawad S, Deakhuntod J. Physical and chemical properties of thai biomass fuels from agricultural residues. The 2nd Joint International Conference on Sustainable Energy and Environment. 2006; 1–23.
[13] Stahl R, Ramadan A B. Fuels and chemicals from rice straw in Egypt. Forschungszentrum Karlsruhe, FZKA-7361, 2007. doi: 10.5445/IR/2700 69896
[14] Niu W J, Huang G Q, Liu X, Chen L J, Han L J. Chemical composition and calorific value prediction of wheat straw at different maturity stages using near-infrared reflectance spectroscopy. Energ Fuel., 2014; 28(12): 7474–7482. doi: 10.1021/ef501446r
[15] Lande S, Riel S V, Høibø O A, Schneider M H. Development of chemometric models based on near infrared spectroscopy and thermogravimetric analysis for predicting the treatment level of furfurylated Scots pine. Wood Sci Technol., 2010; 44(2): 189–203. doi: org/10.1007/s00226-009-0278-x
[16] Sun B L, Liu J L, Liu S J, Yang Q. Application of FT-NIR-DR and FT-IR-ATR spectroscopy to estimate the chemical composition of bamboo (Neosinocalamus affinis Keng). Holzforschung, 2011; 65(5): 689–696. doi: org/10.1515/hf.2011.075
[17] He W M, Hu H R. Prediction of hot-water-soluble extractive, pentosan and cellulose content of various wood species using FT-NIR spectroscopy. Bioresour Technol., 2013; 140: 299–305. doi: org/10.1016 /j.biortech. 2013.04.115
[18] Wójciak A, Kasprzyk H, Sikorska E, Krawczyk A, Sikorski M, Wesełucha-Birczyńska A. FT-Raman, FT-infrared and NIR spectroscopic characterization of oxygen-delignified kraft pulp treated with hydrogen peroxide under acidic and alkaline conditions. Vib Spectrosc., 2014; 71:
62–69. doi: org/10.1016/j.vibspec.2014.01.007
[19] Li X L, Sun C J, Zhou B X, He Y. Determination of hemicellulose, cellulose and lignin in moso bamboo by near infrared spectroscopy. Sci Rep., 2015; 5: 17210. doi: org/10.1038/srep17210
[20] Workman J, Weyer L. Practical guide to interpretive near-infrared spectroscopy. Boca Raton, FL, USA: CRC Press, Inc.2007.
[21] Urbano-Cuadrado M, Castro L D, Pérez-Juan P M, García-Olmo J, Gómez-Nieto M A. Near infrared reflectance spectroscopy and multivariate analysis in enology: determination or screening of fifteen parameters in different types of wines. Anal Chim Acta, 2004; 527: 81–88. doi: org/10.1016/j.aca.2004.07.057
[22] Li J, Tian X H, Wang S X, Ba Y L, Li Y B, Zheng X F. Effects of nitrogen fertilizer reduction on crop yields,soil nitrate nitrogen and carbon contents with straw returning. Journal of Northwest A & F University: Natural Science Edition, 2014; 42: 137–143. (in Chinese)
[23] Xia L L, Xia Y Q, Ma S T, Wang J Y, Wang S W, Zhou W, et al. Greenhouse gas emissions and reactive nitrogen releases from rice production with simultaneous incorporation of wheat straw and nitrogen fertilizer. Biogeosciences, 2016; 13(15): 4569–4579. doi: org/10.5194/ bg-13-4569-2016
[24] Valdez-Vazquez I, Torres-Aguirre G J, Molina C, Ruiz-Aguilar G M L. Characterization of a lignocellulolytic consortium and methane production from untreated wheat straw: Dependence on nitrogen and phosphorous content. BioResources, 2016; 11(2): 4237–4251. doi: 10.15376/biores. 11.2.4237-4251
[2] Jin S Y, Chen H Z. Near-infrared analysis of the chemical composition of rice straw. Ind Crop Prod., 2007; 26(2): 207–211. doi: 10.1016/ j.indcrop.2007.03.004
[3] Huang C, Han L, Yang Z, Liu X. Ultimate analysis and heating value prediction of straw by near infrared spectroscopy. Waste Manage., 2009; 29(6): 1793–1797. doi: 10.1016/j.wasman.2008.11.027
[4] Huang C, Han L J, Liu X, Ma L. The rapid estimation of cellulose, hemicellulose, and lignin contents in rice straw by near infrared spectroscopy. Energ Source Part A., 2010; 33(2): 114–120. doi: org /10.1080/15567030902937127
[5] Hattori T, Murakami S, Mukai M, Yamada T, Hirochika H, Ike M, et al. Rapid analysis of transgenic rice straw using near-infrared spectroscopy. Plant Biotechnol-Nar., 2012; 29: 359–366. doi: org/10.5511/plantbiotech- nology.12.0501a
[6] Sluiter J B, Ruiz R O, Scarlata C J, Sluiter A D, Templeton D W. Compositional analysis of lignocellulosic feedstocks. 1. Review and Description of Methods. J Agric Food Chem., 2010; 58: 9043–9053. doi: 10.1021/jf1008023
[7] Roggo Y, Chalus P, Maurer L, Lema-Martinez C, Edmond A, Jent N. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J Pharmaceut Biomed., 2007; 44: 683–700. doi: org/10. 1016/j.jpba.2007.03.023
[8] Huang C J, Han L J, Yang Z L, Liu X. Prediction of heating value of straw by proximate data, and near infrared spectroscopy. Energ Convers Manage., 2008; 49(12): 3433–3438. doi: 10.1016/j.enconman.2008. 08.020
[9] Belal E B. Bioethanol production from rice straw residues. Braz J Microbiol., 2013; 44: 225–234. doi: 10.1590/S1517-83822013000100033
[10] Liao C P, Wu C Z, Yany Y J, Huang H T. Chemical elemental characteristics of biomass fuels in China. Biomass Bioenerg., 2004; 27(2): 119–130. doi: 10.1016/j.biombioe.2004.01.002
[11] Banta S, Mendoza C. Organic Matter and Rice. Manila, Philippines: International Rice Research Institute, 1984.
[12] Garivait S, Chaiyo U, Patumsawad S, Deakhuntod J. Physical and chemical properties of thai biomass fuels from agricultural residues. The 2nd Joint International Conference on Sustainable Energy and Environment. 2006; 1–23.
[13] Stahl R, Ramadan A B. Fuels and chemicals from rice straw in Egypt. Forschungszentrum Karlsruhe, FZKA-7361, 2007. doi: 10.5445/IR/2700 69896
[14] Niu W J, Huang G Q, Liu X, Chen L J, Han L J. Chemical composition and calorific value prediction of wheat straw at different maturity stages using near-infrared reflectance spectroscopy. Energ Fuel., 2014; 28(12): 7474–7482. doi: 10.1021/ef501446r
[15] Lande S, Riel S V, Høibø O A, Schneider M H. Development of chemometric models based on near infrared spectroscopy and thermogravimetric analysis for predicting the treatment level of furfurylated Scots pine. Wood Sci Technol., 2010; 44(2): 189–203. doi: org/10.1007/s00226-009-0278-x
[16] Sun B L, Liu J L, Liu S J, Yang Q. Application of FT-NIR-DR and FT-IR-ATR spectroscopy to estimate the chemical composition of bamboo (Neosinocalamus affinis Keng). Holzforschung, 2011; 65(5): 689–696. doi: org/10.1515/hf.2011.075
[17] He W M, Hu H R. Prediction of hot-water-soluble extractive, pentosan and cellulose content of various wood species using FT-NIR spectroscopy. Bioresour Technol., 2013; 140: 299–305. doi: org/10.1016 /j.biortech. 2013.04.115
[18] Wójciak A, Kasprzyk H, Sikorska E, Krawczyk A, Sikorski M, Wesełucha-Birczyńska A. FT-Raman, FT-infrared and NIR spectroscopic characterization of oxygen-delignified kraft pulp treated with hydrogen peroxide under acidic and alkaline conditions. Vib Spectrosc., 2014; 71:
62–69. doi: org/10.1016/j.vibspec.2014.01.007
[19] Li X L, Sun C J, Zhou B X, He Y. Determination of hemicellulose, cellulose and lignin in moso bamboo by near infrared spectroscopy. Sci Rep., 2015; 5: 17210. doi: org/10.1038/srep17210
[20] Workman J, Weyer L. Practical guide to interpretive near-infrared spectroscopy. Boca Raton, FL, USA: CRC Press, Inc.2007.
[21] Urbano-Cuadrado M, Castro L D, Pérez-Juan P M, García-Olmo J, Gómez-Nieto M A. Near infrared reflectance spectroscopy and multivariate analysis in enology: determination or screening of fifteen parameters in different types of wines. Anal Chim Acta, 2004; 527: 81–88. doi: org/10.1016/j.aca.2004.07.057
[22] Li J, Tian X H, Wang S X, Ba Y L, Li Y B, Zheng X F. Effects of nitrogen fertilizer reduction on crop yields,soil nitrate nitrogen and carbon contents with straw returning. Journal of Northwest A & F University: Natural Science Edition, 2014; 42: 137–143. (in Chinese)
[23] Xia L L, Xia Y Q, Ma S T, Wang J Y, Wang S W, Zhou W, et al. Greenhouse gas emissions and reactive nitrogen releases from rice production with simultaneous incorporation of wheat straw and nitrogen fertilizer. Biogeosciences, 2016; 13(15): 4569–4579. doi: org/10.5194/ bg-13-4569-2016
[24] Valdez-Vazquez I, Torres-Aguirre G J, Molina C, Ruiz-Aguilar G M L. Characterization of a lignocellulolytic consortium and methane production from untreated wheat straw: Dependence on nitrogen and phosphorous content. BioResources, 2016; 11(2): 4237–4251. doi: 10.15376/biores. 11.2.4237-4251
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Published
2019-04-06
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Diallo, A. A., Yang, Z., Shen, G., Ge, J., Li, Z., & Han, L. (2019). Comparison and rapid prediction of lignocellulose and organic elements of a wide variety of rice straw based on near infrared spectroscopy. International Journal of Agricultural and Biological Engineering, 12(2), 166–172. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/4374
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Renewable Energy and Material Systems
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